Root cause analysis

ABSTRACT

A method and system for performing a root cause analysis. A central processing unit (CPU) tracks a focal point of a user&#39;s eye gaze. The CPU correlates the focal point of the user&#39;s eye gaze to a viewing position of a display device displaying a file that includes event data being viewed by the user. The CPU identifies, as a function of the viewing position, events of interest in the event data and an amount of time that the event data is viewed by the user. The CPU outputs, as a function of a linear regression model, an interest score pertaining to one or more events of interest that were previously identified as a function of the user&#39;s eye gaze. The interest score is a probability of each identified event of interest being a root cause of a defect.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application claiming priority to Ser.No. 16/183,911, filed Nov. 8, 2018, which is a continuation of Ser. No.15/836,973, filed Dec. 11, 2017, U.S. Pat. No. 10,216,565, issued Feb.6, 2019, which is a continuation of Ser. No. 15/248,140 filed Aug. 26,2016, U.S. Pat. No. 10,120,747, issued Nov. 6, 2018.

TECHNICAL FIELD

The present disclosure relates generally to systems, methods and toolsfor performing a root cause analysis.

BACKGROUND

Computer systems experience technical issues, defects, crashes orcomplications that should undergo troubleshooting techniques to removethe causes of abnormal termination or other issues the computer systemis experiencing. Software typically contains a number of defects orerrors which may be classified into two general categories; the first isthe category that causes crashes, while the other category may cause thecomputer system to hang. Among the chief concerns for program developersis to identify software or other system defects that cause computersystems to experience crashing or hanging. Software crashes can range inseverity. In some instances the cause of the crash may be due to fatalsystem errors, which usually result in the abnormal termination of aprogram by a kernel or system thread. Normally, when a crash-causingdefect is discovered, the software provider obtains diagnostic data,attempts to reproduce the error, and, depending on the severity of thedefect, creates and distributes a fix for the defect.

One way to diagnose the cause of the crash involves examining log filescontaining diagnostic data including commands, events, instructions,program errors, computer system hardware specification, and/or otherpertinent diagnostic information. The log file typically is generatedright after a crash has been detected. After a crash, the log file maybe sent to the software provider for analysis. In some cases, a log filedoes not contain enough information to diagnose a problem, thus, a crashdump may be required to troubleshoot the problem. A crash dump isgenerated when the physical contents of the computer system's memory arewritten to a predetermined file location.

SUMMARY

A first embodiment of the present disclosure provides a method forperforming a root cause analysis comprising the steps of: opening, by acentral processing unit (CPU), a file comprising event data; receiving,by the CPU, a recordation of the user's observable while viewing theevent data of the file; identifying, by the CPU, a presence of one ormore events of interest as a function of the user's observable behaviorwhile viewing the event data of the file; calculating, by the CPU, aninterest score for each of the identified events of interest, whereinthe interest score is a probability of each of the identified events ofinterest being a root cause of a defect; and tagging, by the CPU, eachof the events of interest within the file with a tag as a function ofeach calculated interest score.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a schematic view of embodiment of a system for performinga root cause analysis, consistent with the embodiments of the presentdisclosure.

FIG. 2 depicts a schematic view of a network enabled embodiment of asystem for performing a root cause analysis, consistent the embodimentsof the present disclosure.

FIG. 3a illustrates an embodiment of a system for performing a rootcause analysis, collecting observable behavior data of a user in a firstposition.

FIG. 3b illustrates an embodiment of a system for performing a rootcause analysis, collecting observable behavior data of a user in asecond position.

FIG. 3c illustrates an embodiment of a system for performing a rootcause analysis, collecting observable behavior data of a user expressinga first emotive response.

FIG. 3d illustrates an embodiment of a system for performing a rootcause analysis, collecting observable behavior data of a user expressinga second emotive response.

FIG. 4 depicts a flow chart describing an embodiment a method forperforming a root cause analysis.

FIG. 5 depicts an embodiment of a table of entries identifying events ofinterest consistent with the embodiment for performing the root causeanalysis of the present disclosure.

FIG. 6 depicts an embodiment of a Plutchik's wheel of emotion.

FIG. 7 depicts a block diagram of an embodiment of a computer systemcapable of implementing methods for performing a root cause analysis.

DETAILED DESCRIPTION

Overview

Embodiments of the present disclosure recognize that determining theroot cause of a defect, crash or error in a computer system can be timeconsuming. Often, determining the root 3 of 41 cause involves parsingand analysis of domain specific log files, configuration files, crashdumps and other inputs by system operators, support personnel ordevelopers. Embodiments of the present disclosure further recognize thatusers and troubleshooters reviewing the computer system's error filesand logs have varying degrees of expertise, availability and resourcecosts. Subject matter experts (SME) are highly skilled individuals thatare most likely able to quickly determine the root cause of a defect,crash or system error. SMEs however may be a scarce and more costlyresource to utilize. Due to the SMEs scarcity, over-relying on feedbackfrom an SME may bottleneck the identification and resolution of theproblems currently being experienced by a computer system.

Embodiments of present disclosure improve the system error detection andstreamline the focus of the SMF reviewing data files, logs, crash dumpsor other files to reduce the overall amount of time, energy and resourceinvestment that may be required by the SME to identify the root cause ofa system's failure. The embodiments of the present application achievethese improvements in root cause detection by changing the way analystsand troubleshooters that have less experience or training that SMEsinteract with the logs or other error containing files to detectinjection points in the log or other file likely to indicate the rootcause of the system error.

Embodiments of the systems, methods and tools of the present disclosurerecord the observable behavior of users as the user reviews the logfiles or other error containing files. Embodiments of the systems,methods and tools identify behavioral and contextual cues of the userand trouble shooter to infer a level of interest of the reviewer toparticular sections of the files being viewed. Using the reviewers' headmovements, eye movements, facial expressions, and general behaviorrecorded during the review process, embodiments of the systems, methodsand tools are able to identify sections of the reviewed files that aremost likely to be the root cause of the defects and errors. The systems,methods and tools of the present disclosure may numerically rate each ofthe parameters of the sections of interest based on the reviewer'sobserved and/or recorded behavior. Then, based on the numerical valuesassigned, a particular level of importance to each section of the filesreviewed and determined to be of interest to the reviewer may beidentified and a probability of the likelihood that a section of thefile may be a root cause can be calculated.

In some embodiments, the systems tools and methods may tag the sectionsof the reviewed file with markings or color coded tags to draw asubsequent reviewer's attention (such as an SME) to the most relevantcauses of the defect or error. As a result of identifying sections ofinterest by importance from the previous reviewers, a subsequent andpotentially more resource intensive reviewer may focus quickly on theimportant or pertinent sections of the reviewed files. Instead of havingto review the entire file from start to finish. Thus expediting theidentification of the root cause of the system defects or errors andresolving the problem ore efficiently, improving system functionalitywhile reducing the amount of resources expended.

System for Performing Root Cause Analysis

Although certain embodiments are shown and described in detail, itshould be understood that various changes and modifications may be madewithout departing from the scope of the appended claims. The scope ofthe present disclosure will in no way be limited to the number ofconstituting components, the materials thereof, the shapes thereof, therelative arrangement thereof, etc., and are disclosed simply as anexample of embodiments of the present disclosure. A more completeunderstanding of the present embodiments and advantages thereof may beacquired by referring to the following description taken in conjunctionwith the accompanying drawings, in which like reference numbers indicatelike features.

As a preface to the detailed description, it should be noted that, asused in his specification and the appended claims, the singular forms“a”, “an” and “the” include plural referents, unless the context clearlydictates otherwise.

Referring to the drawings, FIG. 1 depicts a block diagram of anembodiment of a system 100 for performing a root cause analysisconsistent with the disclosure of this application. Embodiments of thesystem 100 may be performed by a computer system 101. The computersystem 101 may be a specialized computer system, having specializedconfigurations of hardware, software or a combination thereof asdepicted FIGS. 1-3 d of the present disclosure in some embodiments.Embodiments of the computer system 101 may also comprise one or moreelements of the generic computer system 700 of FIG. 7 as described indetail below. One or more elements of the generic computer system ofFIG. 7, may be integrated into the specialized computer system 101 ofFIGS. 1-3 d.

Embodiments of the computer system 101 of the system 100 for performinga root cause analysis may include a root cause analysis module 103. Theterm “module” may refer to a hardware based module, software basedmodule or a module may be a combination of hardware and softwareresources. A module (whether hardware, software, or a combinationthereof) be designed to implement or execute one or more particularfunctions, tasks or routines. Embodiments of hardware based modules mayinclude self-contained components such as chipsets, specializedcircuitry and one or more memory devices, while a software-based modulemay be part of a program code or linked to program code containingspecific programmed instructions loaded in the memory device 115 of thecomputer system 101 or a remotely accessible memory device (not shown).

Embodiments of the root cause analysis module 103 may utilizespecialized hardware, circuitry or software loaded in the memory device115 or a combination thereof to collect, gather and sort data relatingto the root cause of a system defect, error or failure (hereinaftercollectively referred to as “defects” including recordings of theobservable behavior of users or troubleshooters observing the collecteddata. The root cause analysis module 103 may analyze and identify thesystem environment and circumstances surrounding the root causes of thedefects as a function of the data collected and recorded behavior of theusers. The root cause analysis module 103 may organize the collecteddata to draw conclusions about the importance of events described by thecollected data and subsequently tag files containing the collected datawith identifying information to further assist and focus the attentionof analysts, troubleshooters or SMEs (referred to interchangeably as the“user”) to the most probable root causes of the defects.

In some embodiments, the root cause analysis module 103 may comprise oneor more individual hardware and/or software modules to perform eachtask, routine or service provided by the root cause analysis module 103.For example, in the exemplary embodiment in FIG. 1, the root causeanalysis module 103 may comprise a recording module 105, analyticsmodule 107 and a tagging module 111. Embodiments of the recording module105 may perform the task of collecting and recording audio and visualdata of each user as the user interacts with and reviews a filedescribing events that may have caused the defects to occur. Examples ofthe files being reviewed for the root cause of the defects may includelog files, network files, data files, data dumps, crash logs,configuration files, source code, program code, or any other text baseddata describing conditions, operating environment, code that may lead tosystem errors, defects, crashing or other undesirable effects.

In some embodiments, the recording module 105 may record audio data andvideo data from one or more input devices. For example, in someembodiments the computer system may receive input from a biometric inputdevice 119, a camera, microphone or other input device connected to thecomputer system 101. Embodiments of the biometric input device 119 maybe equipped with specialized facial recognition software capable ofrecording and identifying from recorded video data, the observablebehavior of user, including the user's facial expressions, headmovements, eye movements, direction of the user's eye gaze, the user'semotive reaction and auditory sounds. In some embodiments, the computersystem 101 may be able to identify specific text or informationpresented to the user that is the cause of the observable behavior usinga combination of the recording module 105, analytics module 107 and/orthe biometric input device 119.

Referring to the drawings, FIG. 3a-3d illustrates embodiments of asystem 300 for performing a root cause analysis to identify one moredefects. As demonstrated by the drawings, the computer system 101 ofsystem 300 may include a display device 121 displaying a file beingreviewed by a user 301. Embodiments of the biometric input device 119are shown to be tracking the user's 301 head movements 303 a, 303 b,focal point of a user's gaze/eye movements 305 a, 305 b and changes inemotive expression 309 a, 309 b as the user observes one or more events321 a, 321 b of the file being displayed.

As shown by the comparison between FIGS. 3a and 3b , the biometric inputdevice 119 may use the user's head movements 303 a, 303 b and eyemovements 305 a, 305 b to identify the section 321 a, 321 b of the filethe user 301 is currently viewing on the display device 121. As therecording module 105 records the change in position of the user's headmovements 303 a, 303 b and eye movements 305 a, 305 b, the root causeanalysis module 103 can track which part of the file the user 301 isfocused on viewing and correlate the user's behavior and expressions toidentify which portions of the file are events of interest. An event ofinterest may be a section or portion of the file being analyzed for aroot cause of a defect, error, inconsistency or other problem that mayhave one or more indications that would increase the probability ofbeing the root cause. As depicted by the examples provided in thedrawings, the computer system 101 is shown tracking the user's 301downward head movement 303 a, 303 b and eye movements from a first focalpoint direction 305 a to a second focal point direction 305 b. Based onthe head and eye movements of the user 301, the computer system 101and/or biometric input device 119 may accurately predict the section ofthe file 321 a, 321 b that is currently being focused on by the user301.

FIGS. 3c-3d presents an illustration demonstrating the recordation of anexample of observable behavior that may be tied to the observance of aparticular section of the file being reviewed by the user 301. As shownin FIG. 3c and FIG. 3d , the computer system 101 continues to track thehead movements 303 a, 303 b and eye movements 305 a, 305 b to identifythat the user 301 is viewing section 321 b of the file. The computersystem 101 is further able to identify the change in observable behaviorin the user from expression 309 a to 309 b upon viewing section 321 b ofthe file. The recording module 105 may observe the change in observablebehavior and the computer system 101 may note the change in expression,the type of expression displayed and mark section 321 b of the filebeing viewed at the time of the expression change as a potential eventof interest.

In some embodiments, the computer system 101 may not only rely on theuser's 301 observable behavior to identify events of interest in thefile being reviewed, rather the recording module 105 of the computersystem 101 may further track numerous input devices attached to thecomputer system to further identify events of interest based on userbehavior. For example, keystrokes on a keyboard 320 or mouse 319movements may provide the computer system with important data that maybe collected and recorded by the recording module. For instance, thecomputer system 101 may continue to track head and eye movements, butthe user 301 may use the mouse to further highlight the section of textin the file currently being viewed when an observable expression is madeby the user 301. The computer system 101 may not only make use of thevisual data collected by the biometric input device 119 but furthermoreuse the mouse 319 movement data to further assist with identifying theprecise location of the file the user 301 was observing at the time ofthe change in facial expression.

The input data recorded by the biometric input device 119 and otherinput devices attached to the computer system 101 may be transmitted tothe computer system 101 via an input/output (I/O) interface 117, whereinthe input data received by the computer system 101 may be stored in thememory device 115 of the computer system and/or stored computer-readablestorage device such as a data store 125. An I/O interface 117 may referto any communication process performed between the computer system 101and the environment outside of the computer system 101, for example thebiometric input device 119 and display device 121 and peripheral devicesattached to the computer system, including mouse 319 and keyboard 320.In some embodiments of system 100, the recording module 105 may furtherrecord and store keystrokes or mouse movements performed by a user on akeyboard and/or mouse movements inputted into the computer system 101.The inputted keystrokes, mouse movements and biometric input dataprovided by the biometric input device 119 may all be recorded byrecording module 105 used to correspond observable behavior of the user301 to identify events of interest occurring in the files being reviewedby the user.

Embodiments of the root cause analysis module 103 may further include ananalytics module 107. The analytics module may perform data analysis onthe data recorded by the recording module 105 of the computing system101, including data describing the observable behavior of the userreviewing one or more files. The analytics module 107 may perform astatistical analysis of the data recorded by the recording module 105with the purpose of drawing conclusions about the recorded data and theimplications of the recorded data on the observable behavior of the userand events of interest in the files being reviewed by the user.

The predictive analytics of the analytics module 107 may use a set ofone or more mathematical techniques or algorithms applied to the datarecorded by the recording module 105 to identify events of interest inthe files being reviewed by users as a function of the users observablebehavior including head movements, eye movements, gaze and emotivebehavior during the period of review as well as input data from inputdevices. The analytics module 107 may tabulate the collected data fromthe recording module 105 and identify each section of text being viewedfor each piece of recorded data. In some embodiments, the analyticsmodule may further extract from the recorded data collected by therecording module 105, the events of interest and the correspondingvariables of observable behavior that may have led to the conclusion bythe analytics module 107 that a particular data set is directed towardan event of interest.

To assist the analytics module 107 in performing the mathematicaltechniques and analysis of the many variables present in the recordeddata collected by the recording module 105, the analytics module 107 mayfurther include one or more additional modules or plugins. As shown inthe exemplary embodiment of FIG. 1, the analytics module 107 may furthercomprise a pattern detection module 109 and machine learning tools 110.

Embodiments of the pattern detection module 109 may allow for machinelearning to occur which may focus on the recognition of patterns andregularities in the recorded data and may further allow for theanalytics module 107 to draw conclusions about the recorded data andassociated events f interest in the file being reviewed by one or moreusers. The pattern detection module 109 may draw conclusions aboutpatterns recognized within the recorded data collected from therecording module 105 as well as patterns identified when the analyticsmodule 107 fits the recorded data to one or more statistical models. Insome embodiments, the pattern detection module 109 may be capable ofcorrelating head position, eye movements, gaze and other observablebehaviors of the user reviewing the file to determine a correspondingsection of the file being viewed as well as the corresponding text as afunction of the observable behavior. Examples of pattern recognitionmodels that the pattern detection module 109 may utilize to identifyevents of interest and probability of root cause as a function ofobservable behavior of a user viewing a file may include a lineardiscriminate analysis, statistical classification, regression, sequencelabeling, and statistical inferences.

The machine learning tools 110 of the analytics module 107 may provide acollection of one or more machine learning algorithms which allow forthe analytics module 107 to evaluate the recorded data provided by therecording module to identify events of interest and ultimatelydetermining the root cause of a defect, error or other irregularity. Thepattern detection module 109 may use one or more of the machine learningmodels to draw conclusions and recognize patterns in the recorded data.The patterns may identify events of interest based on the patterns ofprevious users as well as knower behavioral patterns of particularusers.

There are two general styles of algorithms that may be included as partof the machine learning models. The first grouping of algorithms may bereferred to as “learning style” algorithms, while the second groupingmay be referred to as algorithms that draw conclusions by similarity orfunction. Learning style algorithms may include supervised learning,unsupervised learning and semi-supervised learning algorithms, whilealgorithms that draw conclusions by learning or function may includeregression algorithms, instance based algorithms, regularizationalgorithms, decision tree algorithms, Bayesian algorithms, clusteringalgorithms, association rule learning algorithms, artificial neuralnetwork algorithms, deep learning algorithms, dimensionality reductionalgorithms and ensemble algorithms.

Supervised learning algorithms may refer to analytics techniques andalgorithms that use training data where known input data having a knownlabel or result teach the machine how to interpret unknown data beingfed to the analytics engine. The supervised learning model places theanalytics module 107 through a training process where the analyticsmodule 107 may make predictions about events of interest derived fromobservable data and is corrected when the predictions are wrong. Thetraining process may continue until the model achieves a desired levelof accuracy on the training data. Examples algorithms that usesupervised learning techniques include logistic regression and backpropagation neural network.

Unsupervised learning is an analytics technique where input data is notlabeled or does not have a known result. Instead, a model is prepared bydeducing structures present in the input data. This technique may resultin extraction of general rules. The algorithms, through a mathematicalprocess, may systematically reduce redundancy, or it may be used toorganize data by similarity. Example algorithms include Apriorialgorithm and k-Means.

In some embodiments, regression algorithms may be used as an analyticsmodel for machine learning. Regression modeling models the relationshipbetween variables that are then refined using a measure of error in thepredictions made by the model. Some exemplary regression algorithms thatmay be used by the analytics module 107 may include ordinary leastsquare regression (OLSR), linear regression, logistic regression,stepwise regression, multivariate adaptive regression splines (MARS),and locally estimated scatterplot smoothing (LOESS).

Instance based learning models a decision problem with instances orexamples of training data that are deemed important or required to themodel. Such methods typically build up a database of example data andcompare new data to the database using a similarity measure in order tofind the best match and make a prediction. Focus is put onrepresentation of the stored instances and similarity measures usedbetween instances. Examples of this instance based learning algorithmsmay include k-nearest neighbor (KNN), learning vector quantization(LVQ), self-organizing map (SOM) and locally weighted learning (LVL).

Regularization algorithms are algorithms that act as an extension toother algorithms described in this application, usually regressiontechniques. The regularization algorithm may penalize models based ontheir complexity, by favoring simpler models that are more efficient atgeneralizing. Regularization algorithms may include ridge regression,least absolute shrinkage and selection operator (LASSO), elastic net andleast-angle regression (LARS).

Decision tree algorithms may be one exemplary algorithm used by theanalytics engine, in addition to the k-clustering techniques describedabove. Decision tree methods construct a model of decisions based on theactual values and attributes of the user data collected and provided tothe analytics engine. The decision tree algorithm forks in treestructures until a prediction decision is made for a given record beinganalyzed. Decision trees may be trained on data for classification andregression problems. Examples of decision tree algorithms that may beused and applied to the collected data may include classification andregression tree (CART), iterative Dichotomiser 3 (ID3, C4.5/C.5.0,chi-squared automatic interaction detection (CHAID), decision stump, M5and conditional decision trees.

Bayesian algorithms are methods that explicitly apply Bayes' theorem.These algorithms may generally be used for solving problems such asclassification and regression. Popular Bayesian algorithms may includenaïve Bayes, Gaussian naïve Bayes, multinomial naïve Bayes, averagedone-dependence estimators (AODE), Bayesian belief network (BBN) andBayesian network (BN).

Clustering algorithms are generally directed toward created models thatorganize data collected into a centroid based and hierarchal structure.These methods, as described above when discussing k-means clustering,use the inherent structures in the data to organize the data into groupsthat have the maximum amount of commonality between the groups and themaximum amount of differences between groups. In addition to k-meansclustering, other types of clustering algorithms may include k-medians,expectation maximization (EM), and hierarchical clustering.

Much like clustering algorithms, dimensionality reduction algorithmsseek and exploit the inherent structure in the data, but in this case inan unsupervised manner to summarize, order and describe data using lessinformation. Dimensionality reduction algorithms can be helpful tovisualize dimensional data or to simplify data which can then be used ina supervised learning method. Many of these dimensional methods can beadapted for use alongside classification and regression techniques.Examples of dimensionality reduction algorithms may include PrincipalComponent Analysis (PCA), Principal Component Regression (PCR), PartialLeast Squares Regression (PLSR), Sammon Mapping, MultidimensionalScaling (MDS), Projection Pursuit, Linear Discriminant Analysis (LDA),Mixture Discriminant Analysis (MDA), Quadratic Discriminant Analysis(QDA), and Flexible Discriminant Analysis (FDA).

Embodiments of the recorded data received by the analytics module 107may be received from one or more data sources, for example the biometricinput device 119, mouse, keyboard, pointer device, webcam etc. Theinformation provided to the analytics module 107 may then besubsequently predictively analyzed, stored and/or retained by thecomputer system 101 in one or more databases and/or data warehouses suchas data store 125 and may be recalled or queried at a later point intime. The data being recorded by the recording module 105 may betransmitted to the analytical module 107 continuously as a constantstream of data from one or more data sources, at discrete time intervalsproviding refreshed updates upon request by the analytics module 107 orat a moment when the information of an inputted a data source has beenupdated to reflect a change.

Embodiments of the analytics module 107 may use pattern detection andmachine learning techniques to organize the recorded data intomeaningful conclusions and generate a probability of an event ofinterest being a root cause of an error, defect or other problem. FIG. 5provides an example of a table resulting from the organization andanalysis of the recorded data collected by the recording module andanalyzed by the analytics module 107. The tabulated data organized bythe analytics module may be calculated as a function of a user'sobservable behavior to identify one or more events of interest, generateone or more properties identifying each event of interest and finallycalculating an interest score as a function of the recorded data thatmay include the observable behavior of the user.

As shown in FIG. 5, the analytics module may identify one or moreproperties of each event of interest to organize and calculate aninterest score 509. Properties of each event of interest may include thescreen position 501 where the event of interest may have been identifiedin a filed, the amount of time 503 that the event of interest was viewedor focused on by a user, the emotive expression 505 of the user duringthe time period 503 that the event of interest was viewed and the actualtext of the event data 507, specifically describing the section of thefile being viewed by the user.

The property of screen position 501 may be determined by the analyticsmodule 107 by overlaying or assigning a grid or table to each of thepositions of the display device 121 display area. For example, thescreen position 501 may be based on dividing the display device'sdisplay area into a Cartesian plane having X, Y coordinates in someembodiments. In alternative embodiments, the screen position may bebased on a series of rows and columns, each having a pre-defined spaceon the display area of the display device 121. For example, definingeach row by letters A-Z, AA-ZZ etc., and each column as an integer 1-n,where n is the last column number in the sequence of numbered columns.For instance, A1 is the section of the screen denoting the first row,first column, whereas A2 is the first row, second column. Likewise,screen position C1 may be the third row, first column. In somealternative embodiments, the screen position may be defined absolutelyby the location or range of location of the specific pixels contributingto the formation of the text displayed by the display device 121.

Embodiments of the viewing time 503 for each event of interest may becalculated by the analytics module as a function of the head movements,eye movements and gaze of the user. The analytics module 107 maycalculate the section of the file that the user is focusing and theassociated text of the event data 507 and may continuously track thefocus of the user until the user moves the user's focus to a differentsection of the file. In some embodiments, the analytics module 107 mayfurther track a user's focus of the head and eye movements to identifythe length of time and number of times a user's focus returns back to aparticular set of text in the file being reviewed. The observablebehavior of the user's focus diverting back a particular text of eventdata 507 may be a further indicator of an event of interest and becalculated as part of the viewing time 503 because the cumulative amountof time viewed 503 may be factored into or impact the overall interestscore 509.

The emotive expression 505 may identify the changes in the user'semotions as the user is reviewing the file for the root cause of thedefects. Changes in emotion, such as facial expression can provide cluesinto the cognition of the reviewing user and provide clues to whetherparticular sections of the file's text are being considered events ofinterest by the user. The strength of the emotive expression of eachuser may vary. Stronger expressions, whether happy, sad, angry, joyfuletc. may provide stronger indications that an event of interest has beenidentified and may increase the likelihood that a root cause of a defecthas been identified. For example, a strong indication of happiness mayidentify to the analytics module 107 that a user may have identifiedwhat the user believes to be the root cause of the defects. Conversely,a strong emotive expression of anger may also identify a higherlikelihood that a root cause has been identified. For instance, thedefect presented as text of event data 507 may be glaringly obvious orincorrect source code that should have been identified before the defectoccurred.

In some embodiments, the emotions of the reviewing user identified byanalytics module may be selected from and compared to the emotions of aPlutchik's wheel of emotion 600, as shown in FIG. 6. The closer theemotive expression of the user is identified by the analytics module 107to the center of the Plutchik wheel 600, the more of an impact theemotion may have on the likelihood of an event of interest being theroot cause of the defect, and thus a higher interest score. On the otherhand, the further from the center the identified emotive expression 505of the user, the weaker the analytics module 107 may consider theemotive expression 505 and thus the emotive expression may have a lowerimpact on the interest score and ultimately a lower probability that theevent of interest tied to the weaker emotive expression 505 is the rootcause of the defects.

Embodiments of the analytics module 107 may be programmed to considerthe variations of emotion that may be expressed by different users whomay be reviewing each file. Embodiments of the computer system 101 maylearn through the machine learning tools 110 and the pattern recognitionmodule 109 the emotion ranges of each user. The computer system 101 maybe able to scale or normalize more and less emotional or expressiveindividual users reviewing the files. The analytics module 107 mayprogressively learn the temperaments of the users reviewing the filesand adjust the overall interest score accordingly. More emotional orless emotional users may be identified by the interest scores obtainedover one or more different reviews of different files. For example, moreemotionally expressive user may have consistently higher interest scoresfor each event of interest identified within a particular file, whereasa less emotional user may have consistently lower interest scoresspanning across the events of interest in a particular file. In someembodiments, if the analytics module 107 identifies a user has having aset of interest scores for a particular file or across a series ofunrelated files or reviewing assignments, that are consistently higheror lower than the average reviewer or a set threshold average, theanalytics module 107 may apply a correction factor to normalize theinterest scores of the particular user.

Embodiments of the interest score 509 may calculated by the analyticsmodule 107 as a function of the recorded data collected and organizedinto one or more properties identified by the analytics module 107. Eachof the properties may be assigned a numerical value based on astatistical model stored by the pattern detection module 109. Forexample, the model may be a linear regression model such as the one usedto calculate the interest score 509 in FIG. 5. For instance, the samplemodel used to calculate the interest scores 509 in FIG. 5 was a linearregression model wherein the interest score=0.456(screenposition)+0.674(time)+0.967(emotive expression)+0.231 (event text). Inthe sample model used to calculate the interest scores in FIG. 5, theviewing time 503 and the value allotted to the emotive expression 505were each weighted higher than the value of the screen position 501 orevent text 507. However, changes in the values, weighting of eachproperty in the regression formula and/or type sample model used mayvary.

Embodiments of the interest score 509 may be considered a probabilitythat the event of interest identified is the root cause of the defectsoccurring. For instance, in some embodiments, the interest score may bea value from 0 to 1 inclusively as shown in FIG. 5. Comparing each ofthe interest scores side by side, the interest score 509 with thehighest comparative value may be considered to be the most likely rootcause of the defects being reviewed by the users. In some embodiments,the interest score may be converted into a percentage of probability ora percentage of certainty. For example, 0.55 may equal 55%, 0.85=85%,0.3=30%, etc.

Embodiments of the root cause analysis module may further include atagging module 111. The tagging module 111 may perform the task orfunction of adding identifying tags to one or more sections of eventdata 507 within the file being reviewed by the user. In someembodiments, the tags may be automatically generated by the taggingmodule 111 as a function of the interest score. Event data 507 may betagged with different tags of various colors, sizes, information,abbreviations, or symbols to draw the user's attention to the event dataand the type of tag added to the file may vary depending on how high orlow the interest score may be. For instance an event of interest havingan interest score of 0.8 to 1 may be tagged with identifiable tag in thefile that alerts a user or subsequent user of the severity of the event.On the other hand, an event of interest having an interest score between0.1 to 0.2 may have a different tag alerting a user of the event havinga probability of being the root cause, but much less likely to be assevere of an event than the previous event with the score of 0.8 to 1.

A person skilled in the art should recognize that any number of rangesor cut off points in the interest score scale may be set for individualtags of increasing severity or importance. Any number of tag types canbe pre-set and generated as a function of the interest score. Forexample, a different type of tag indicating an increasing level ofseverity may be set for every 0.01, 0.05. 0.1, 0.2, 0.3, 0.5, etc.,increase in interest score. For instance using 0.1 as prototypicalexample, each different type of tag may generate as a function of theinterest score between 0 to <0.1, 0.1 to <0.2, 0.2 to <0.3, 0.3 to <0.4,0.4 to <0.5, 0.5 to <0.6, 0.6 to <0.7, 0.7 to <0.8, 0.8 to <0.9 and 0.9to 1.0. The action of tagging the file being reviewed may alertsubsequent users reviewing the file to pay particular attention to thetagged section or allow for a higher resource intensive user such as anSME to review the most severe tags first in an effort to focus on themost likely root causes of the defects or errors.

In some embodiments of the root cause analysis system, the system 200and the computer system 101 may be connected to a network 210 via anetwork interface controller 124. for example, allowing the computersystem to access network enabled resources for performing the root causeanalysis. The network 210 may refer to a group of network-accessiblecomputer systems 201 a, 201 b . . . 201 n (hereinafter referredcollectively as network-accessible computer systems) or other computinghardware devices, such as a network accessible data store 225, linkedtogether through communication channels to facilitate communication andresource sharing among the computer systems and hardware devices.Elements depicted in the figures having reference numbers includingsub-letters and ellipses, for example network-accessible computer system201 a, 201 b . . . 201 n may signify that the embodiments comprising theelement are not limited only to the amount of elements actually shown inthe drawings, but rather, the ellipses between the letters and then^(th) element indicate a variable number of similar elements of asimilar type that may be present. In this case, each of the computersystems 201 may include some or all of the elements described above thatmay be present in or connected to computer system 101. Examples ofnetwork 210 may include a local area network (LAN), home area network(HAN), wide area network (WAN), back bone networks (BBN), peer to peernetworks (P2P), campus networks, enterprise networks, the Internet,cloud computing networks and any other network known by a person skilledin the art.

In some embodiments, multiple users may perform a root cause analysis ateach of the computer systems 101, 201 connected to the network 210. Eachof the files being review may be stored locally or remotely accessibleto one or more of the computer systems 101, 201 or the files beingreviewed on the network 210 may be remotely stored in anetwork-accessible data store 225. Furthermore, in some embodiments, acomputer system 101, 201 may not only use a network 210 for the purposeof allowing review of a file at multiple locations, eithersimultaneously or non-simultaneously, the system 200 may further becapable of leveraging the processing power and other resources of thecomputer network 210 to increase the computing capabilities of resourceintensive functions such as the analytics module 107 to reduce the timeit may take to analyze recorded data of collected by the recordingmodule.

In alternative embodiments, each computer system 101, 201 of the network210 having a reviewer reviewing a file for events of interest, may berecording and collecting data. The collected data from each reviewer maybe collected to a central location or storage device such asnetwork-accessible data store 225. Subsequently the aggregation ofobservable behavior recorded by each computer system 101, 201 for eachreviewer may be analyzed as a whole, rather than on a user by userbasis, to generate a single table of interest scores spanning multipleuser reviews of the file. Under such embodiments, events of interestthat are in common between multiple reviewers may increase the interestscore for the particular event of interest that was identified by aplurality of reviewers, indicating a higher likelihood that the event isthe root cause of a defect or error.

Method for Performing Root Cause Analysis

The drawing of FIG. 4 represents an embodiment 400 of a method oralgorithm that may be implemented for performing a root cause analysisin accordance with the root cause analysis systems described in FIGS.1-3 d using one or more computers as defined generically in FIG. 7below, and more specifically by the specific embodiments of FIGS. 1-3 d,A person skilled in the art should recognize that the steps of thealgorithm described in FIG. 4 may be performed in a different order thanpresented by FIG. 4 and the algorithm may not require all of the stepsdescribed herein to be performed. Rather some embodiments may implementa method for performing a root cause analysis using only one or more ofthe steps discussed below.

The method of FIG. 4 may initiate in step 401 upon opening a filecomprising event data for review by a user. The user may select and opena file to review such as network files, data files, data dumps, crashlogs, configuration files, source code, program code, or any other textbased data describing conditions, operating environment or program codethat may lead to system errors, defects, crashing or other undesirableeffects. Upon opening the file in step 401, or upon manually activatingthe recording module 105 of the computer system 101 by the userperforming the review of the file on (either locally or remotely), thecomputer system 101 may in step 403 start recording the user'sobservable behavior via an input device such as a biometric input device119, mouse 319, keyboard 320, camera or microphone, while the user isviewing the opened file.

In some embodiments of the method, the recording module 105 collectingthe recorded data, may transmit or stream the recorded data to theanalytics module 107. In step 405 of the method for performing a rootcause analysis, the analytics module 107 may apply analytical methodsand models to the recorded data to continuously analyze the user'sobservable behavior in order to identify cues from the user's observablebehavior that may indicate a presence of an event of interest to theuser or other reviewers of the file tasked with identifying the rootcause of a defect or error. In step 407, the analytics module 107 maydraw a conclusion whether or not the recorded data of the user'sobservable behavior suggests an event of interest. If, in step 407, anevent of interest has not been identified, the analytics module 107 maycontinue to analyze the recorded data being received from the recordingmodule 105 in an effort to identify an event of interest that mayindicate a root cause of a defect or error.

Conversely, if an event of interest to the analytics module 107 isidentified in step 407, the analytics module 107 may store the event ofinterest identified in the file, and the corresponding parameters of theuser's observable behavior that lead to the conclusion of the event databeing of interest to identifying the root cause. The computer system 101may store the recorded data and/or the parameters of the event ofinterest in a data store 125, network-accessible data store 225, datawarehouse, data mart, etc. for further processing, query of analysis.For example, in some embodiments, the parameters of the event ofinterest identified from the recorded data may be tabulated and storedin a database for further calculations and querying by a user. FIG. 5depicts an example of tabulated database entries for a plurality ofevents of interest and each corresponding parameter identified, such asscreen position 501, time viewed 503, emotive expression 505 and textcorresponding to the event data 507.

In step 409, the analytics module 107 may assign a numerical value toeach of the parameters of the user's observable behavior. The numericalvalue assigned to each parameter may vary depending on the type ofstatistical methods, models and algorithms being used or applied to theparameter data. Once the observable behaviors, such as the emotiveexpression 505 of the user have been correlated to a numerical value, instep 411 the computer system 101 may calculate an interest score foreach event of interest. The interest scores may be calculated as afunction of the observable behavior of the user and the correspondingnumerical values assigned for the user's particular observablebehaviors. Each of the generated interest scores may be compared todetermine a range of the most probable to least probable events ofinterest that may be the root cause of the defects, errors or otherproblems being identified by the user.

In step 413, the computer system 101 may generate and assign a tag tothe text of the event data in the reviewed file for one or more of theevents of interest having an interest score calculated in step 411. Eachof the tags generated and assigned in step 413 may be generated as afunction of the interest score, wherein changes in the value or rangesof the interest score may result in the generation of different tagsbeing applied to the event data of the file. In some embodiments, thetype of tag generated in step 413 may be dependent on the increasingseverity and likelihood that the event data of the file is the rootcause of the problem or defect.

In step 415, each tag generated in step 413 may be applied to thecorresponding event data of the file for each of the events of interestidentified in step 407. In some embodiments the type, size, color, orindicator of tag to the event data of a file may be performed as afunction of the increasing severity and likelihood that the event datain the file is the root cause of a problem or defect. For example,events of interest having a higher severity or likelihood of being theroot cause of a defect may be larger, more brightly colored or moreattention grabbing than a tag applied to a less severe event ofinterest.

Computer System

Referring to the drawings, FIG. 7 illustrates a block diagram of acomputer system 700 that may be included in the systems of FIGS. 1-3 dand for implementing methods for performing a root cause analysis asshown in the embodiment of FIG. 4 and in accordance with the embodimentsof the present disclosure. The computer system 700 may generallycomprise a processor, otherwise referred to as a central processing unit(CPU) 791, an input device 792 coupled to the processor 791, an outputdevice 793 coupled to the processor 791, and memory devices 794 and 795each coupled to the processor 791. The input device 792, output device793 and memory devices 794, 795 may each be coupled to the processor 791via a bus. Processor 791 may perform computations and control thefunctions of computer 700, including executing instructions included inthe computer code 797 for tools and programs for performing a root causeanalysis, in the manner prescribed by the embodiments of the disclosureusing the systems of FIGS. 1-3, wherein the instructions of the computercode 797 may be executed by processor 791 via memory device 795. Thecomputer code 797 may include software or program instructions that mayimplement one or more algorithms for implementing the methods forperforming a root cause analysis, as described in detail above. Theprocessor 791 executes the computer code 797. Processor 791 may includea single processing unit, or may be distributed across one or moreprocessing units in one or more locations (e.g., on a client andserver). The memory device 794 may include input data 796. The inputdata 796 includes any inputs required by the computer code 797. Theoutput device 793 displays output from the computer code 797. Either orboth memory devices 794 and 795 may be used as a computer usable storagemedium (or program storage device) having a computer readable programembodied therein and/or having other data stored therein, wherein thecomputer readable program comprises the computer code 797. Generally, acomputer program product (or, alternatively, an article of manufacture)of the computer system 700 may comprise said computer usable storagemedium (or said program storage device).

Memory devices 794, 795 include any known computer readable storagemedium, including those described in detail below. In one embodiment,cache memory elements of memory devices 794, 795 may provide temporarystorage of at least some program code (e.g., computer code 797) in orderto reduce the number of times code must be retrieved from bulk storagewhile instructions of the computer code 797 are executed. Moreover,similar processor 791, memory devices 794, 795 may reside at a singlephysical location, including one or more types of data storage, or bedistributed across a plurality of physical systems in various forms.Further, memory devices 794, 795 can include data distributed across,for example, a local area network (LAN) or a wide area network (WAN).Further, memory devices 794, 795 may include an operating system (notshown) and may include other systems not shown in FIGS. 1-6.

In some embodiments, the computer system 700 may further be coupled toan Input/output (I/O) interface and a computer data storage unit. An I/Ointerface may include any system for exchanging information to or froman input device 792 or output device 793. The input device 792 may be,inter alia, a keyboard, a mouse, sensors, biometric input device,camera, etc. The output device 793 may be, inter alia, a printer, aplotter, a display device (such as a computer screen or monitor), amagnetic tape, a removable hard disk, a floppy disk, etc. The memorydevices 794 and 795 may be, inter alia, a hard disk, a floppy disk, amagnetic tape, an optical storage such as a compact disc (CD) or adigital video disc (DVD), a dynamic random access memory (DRAM), aread-only memory (ROM), etc. The bus may provide a communication linkbetween each of the components in computer 200, and may include any typeof transmission link, including electrical, optical, wireless, etc.

An I/O interface may allow computer system 700 to store information(e.g., data or program instructions such as program code 797) on andretrieve the information from computer data storage unit (not shown).Computer data storage unit includes a known computer-readable storagemedium, which is described below. In one embodiment, computer datastorage unit may be a non-volatile data storage device, such as amagnetic disk drive (i.e., hard disk drive) or an optical disc drive(e.g., a CD-ROM drive which receives a CD-ROM disk).

As will be appreciated by one skilled in the art, in a first embodiment,the present invention may be a method; in a second embodiment, thepresent invention may be a system; and in a third embodiment, thepresent invention may be a computer program product. Any of thecomponents of the embodiments of the present invention can be deployed,managed, serviced, etc. by a service provider that offers to deploy orintegrate computing infrastructure with respect to accessing content ofa shared account. Thus, an embodiment of the present invention disclosesa process for supporting computer infrastructure, where the processincludes providing at least one support service for at least one ofintegrating, hosting, maintaining and deploying computer-readable code(e.g., program code 797) in a computer system (e.g., computer 700)including one or more processor(s) 791, wherein the processor(s) carryout instructions contained in the computer code 797 causing the computersystem to automatically configure multiple display devices. Anotherembodiment discloses a process for supporting computer infrastructure,where the process includes integrating computer-readable program codeinto a computer system including a processor.

The step of integrating includes storing the program code in acomputer-readable storage device of the computer system through use ofthe processor. The program code, upon being executed by the processor,implements a method of accessing content of a shared account. Thus thepresent invention discloses a process for supporting, deploying and/orintegrating computer infrastructure, integrating, hosting, maintaining,and deploying computer-readable code into the computer system 700,wherein the code in combination with the computer system 700 is capableof performing a method for performing a root cause analysis.

A computer program product of the present invention comprises one ormore computer readable hardware storage devices having computer readableprogram code stored therein, said program code containing instructionsexecutable by one or more processors of a computer system to implementthe methods of the present invention.

A computer program product of the present invention comprises one ormore computer readable hardware storage devices having computer readableprogram code stored therein, said program code containing instructionsexecutable by one or more processors of a computer system to implementthe methods of the present invention.

A computer system of the present invention comprises one or moreprocessors, one or more memories, and one or more computer readablehardware storage devices, said one or more hardware storage devicescontaining program code executable by the one or more processors via theone or more memories to implement the methods of the present invention.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable circuitry, field-programmable gate arrays(FPGA), or programmable logic arrays (PLA) may execute the computerreadable program instructions by utilizing state information of thecomputer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for performing a root cause analysis,said method comprising: tracking, by a central processing unit (CPU), afocal point of a user's eye gaze; correlating, by the CPU, the focalpoint of the user's eye gaze to a viewing position of a display devicedisplaying a file that includes event data being viewed by the user;identifying, by the CPU, as a function of the viewing position, eventsof interest in the event data and an amount of time that the event datais viewed by the user; identifying, by the CPU, an emotive expression ofthe user during an amount of time the user's eye glaze is focused on theviewing position, inserting, by the CPU, a numerical value assigned tothe viewing position, the amount of time, the emotive expression andtext of the event data, into a linear regression model; and outputting,by the CPU, as a function of the linear regression model, an interestscore pertaining to one or more events of interest that were previouslyidentified as a function of the user's eye gaze, said interest scorebeing a probability of each identified event of interest being a rootcause of a defect.
 2. The method of claim 1, said method furthercomprising: recording, by the CPU, recordation data of the user's eyegaze while viewing the event data of the file.
 3. The method of claim 1,wherein the file including event data is selected from the groupconsisting of a log file, a configuration file, and source code.
 4. Themethod of claim 1, wherein the linear regression model outputs a valueof the interest score between 0-1 inclusively.
 5. The method of claim 1,said method further comprising: generating a color-coded tag in thefile, based on severity of the one or more events of interest.
 6. Themethod of claim 1, said method further comprising: tagging, by the CPU,each event of interest within the file with a tag as a function of eachinterest score.
 7. The method of claim 1, wherein the closer the emotiveexpression of the user is to a center of a Plutchik wheel of emotion,the higher is the probability that one or more of the identified eventsof interest is the root cause of the defect.
 8. A computer programproduct comprising: one or more computer readable hardware storagedevices having computer readable program code stored therein, saidprogram code containing instructions executable by a central processingunit (CPU) to implement a method for performing a root cause analysis,said method comprising: tracking, by the CPU, a focal point of a user'seye gaze; correlating, by the CPU, the focal point of the user's eyegaze to a viewing position of a display device displaying a file thatincludes event data being viewed by the user; identifying, by the CPU,as a function of the viewing position, events of interest in the eventdata and an amount of time that the event data is viewed by the user;identifying, by the CPU, an emotive expression of the user during anamount of time the user's eye glaze is focused on the viewing position,inserting, by the CPU, a numerical value assigned to the viewingposition, the amount of time, the emotive expression and text of theevent data, into a linear regression model; and outputting, by the CPU,as a function of the linear regression model, an interest scorepertaining to one or more events of interest that were previouslyidentified as a function of the user's eye gaze, said interest scorebeing a probability of each identified event of interest being a rootcause of a defect.
 9. The computer program product of claim 8, saidmethod further comprising: recording, by the CPU, recordation data ofthe user's eye gaze while viewing the event data of the file.
 10. Thecomputer program product of claim 8, wherein the file including eventdata is selected from the group consisting of a log file, aconfiguration fife, and source code.
 11. The computer program product ofclaim 8, wherein the linear regression model outputs a value of theinterest score between 0-1 inclusively.
 12. The computer program productof claim 8, said method further comprising: generating a color-coded tagin the file, based on severity of the one or more events of interest.13. The computer program product of claim 8, said method furthercomprising: tagging, by the CPU, each event of interest within the filewith a tag as a function of each interest score.
 14. The computerprogram product of claim 8, wherein the closer the emotive expression ofthe user is to a center of a Plutchik wheel of emotion, the higher isthe probability that one or more of the identified events of interest isthe root cause of the defect.
 15. A computer system, comprising: acentral processing unit (CPU); a memory device coupled to the CPU; and acomputer readable storage device coupled to the processor, wherein thestorage device contains program code executable by the CPU via thememory device to implement a method for performing a root causeanalysis, said method comprising: tracking, by the CPU, a focal point ofa user's eye gaze; correlating, by the CPU, the focal point of theuser's eye gaze to a viewing position of a display device displaying afile that includes event data being viewed by the user; identifying, bythe CPU, as a function of the viewing position, events of interest inthe event data and an amount of time that the event data is viewed bythe user; identifying, by the CPU, an emotive expression of the userduring an amount of time the user's eye glaze is focused on the viewingposition, inserting, by the CPU, a numerical value assigned to theviewing position, the amount of time, the emotive expression and text ofthe event data, into a linear regression model; and outputting, by theCPU, as a function of the linear regression model, an interest scorepertaining to one or more events of interest that were previouslyidentified as a function of the user's eye gaze, said interest scorebeing a probability of each identified event of interest being a rootcause of a defect.
 16. The computer system of claim 15, said methodfurther comprising: recording, by the CPU, recordation data of theuser's eye gaze while viewing the event data of the file.
 17. Thecomputer system of claim 15, wherein the file including event data isselected from the group consisting of a log file, a configuration file,and source code.
 18. The computer system of claim 15, wherein the linearregression model outputs a value of the interest score between 0-1inclusively.
 19. The computer system of claim 15, said method furthercomprising: generating a color-coded tag in the file, based on severityof the one or more events of interest.
 20. The computer system of claim15, said method further comprising: tagging, by the CPU, each event ofinterest within the file with a tag as a function of each interestscore.